41,441 research outputs found
FACT – a framework for the functional interpretation of high-throughput experiments
BACKGROUND: Interpreting the results of high-throughput experiments, such as those obtained from DNA-microarrays, is an often time-consuming task due to the high number of data-points that need to be analyzed in parallel. It is usually a matter of extensive testing and unknown beforehand, which of the possible approaches for the functional analysis will be the most informative RESULTS: To address this problem, we have developed the Flexible Annotation and Correlation Tool (FACT). FACT allows for detection of important patterns in large data sets by simplifying the integration of heterogeneous data sources and the subsequent application of different algorithms for statistical evaluation or visualization of the annotated data. The system is constantly extended to include additional annotation data and comparison methods. CONCLUSION: FACT serves as a highly flexible framework for the explorative analysis of large genomic and proteomic result sets. The program can be used online; open source code and supplementary information are available at
Yeast Protein Interactome Topology Provides Framework for Coordinated-Functionality
The architecture of the network of protein-protein physical interactions in
Saccharomyces cerevisiae is exposed through the combination of two
complementary theoretical network measures, betweenness centrality and
`Q-modularity'. The yeast interactome is characterized by well-defined
topological modules connected via a small number of inter-module protein
interactions. Should such topological inter-module connections turn out to
constitute a form of functional coordination between the modules, we speculate
that this coordination is occurring typically in a pair-wise fashion, rather
than by way of high-degree hub proteins responsible for coordinating multiple
modules. The unique non-hub-centric hierarchical organization of the
interactome is not reproduced by gene duplication-and-divergence stochastic
growth models that disregard global selective pressures.Comment: Final, revised version. 13 pages. Please see Nucleic Acids open
access article for higher resolution figure
Type-driven automated program transformations and cost modelling for optimising streaming programs on FPGAs
In this paper we present a novel approach to program optimisation based on compiler-based type-driven program transformations and a fast and accurate cost/performance model for the target architecture. We target streaming programs for the problem domain of scientific computing, such as numerical weather prediction. We present our theoretical framework for type-driven program transformation, our target high-level language and intermediate representation languages and the cost model and demonstrate the effectiveness of our approach by comparison with a commercial toolchain
ProbCD: enrichment analysis accounting for categorization uncertainty
As in many other areas of science, systems biology makes extensive use of statistical association and significance estimates in contingency tables, a type of categorical data analysis known in this field as enrichment (also over-representation or enhancement) analysis. In spite of efforts to create probabilistic annotations, especially in the Gene Ontology context, or to deal with uncertainty in high throughput-based datasets, current enrichment methods largely ignore this probabilistic information since they are mainly based on variants of the Fisher Exact Test. We developed an open-source R package to deal with probabilistic categorical data analysis, ProbCD, that does not require a static contingency table. The contingency table for
the enrichment problem is built using the expectation of a Bernoulli Scheme stochastic process given the categorization probabilities. An on-line interface was created to allow usage by non-programmers and is available at: http://xerad.systemsbiology.net/ProbCD/. We present an analysis framework and software tools to address the issue of uncertainty in categorical data analysis. In particular, concerning the enrichment analysis, ProbCD can accommodate: (i) the stochastic nature of the high-throughput experimental techniques and (ii) probabilistic gene annotation
Sparse Proteomics Analysis - A compressed sensing-based approach for feature selection and classification of high-dimensional proteomics mass spectrometry data
Background: High-throughput proteomics techniques, such as mass spectrometry
(MS)-based approaches, produce very high-dimensional data-sets. In a clinical
setting one is often interested in how mass spectra differ between patients of
different classes, for example spectra from healthy patients vs. spectra from
patients having a particular disease. Machine learning algorithms are needed to
(a) identify these discriminating features and (b) classify unknown spectra
based on this feature set. Since the acquired data is usually noisy, the
algorithms should be robust against noise and outliers, while the identified
feature set should be as small as possible.
Results: We present a new algorithm, Sparse Proteomics Analysis (SPA), based
on the theory of compressed sensing that allows us to identify a minimal
discriminating set of features from mass spectrometry data-sets. We show (1)
how our method performs on artificial and real-world data-sets, (2) that its
performance is competitive with standard (and widely used) algorithms for
analyzing proteomics data, and (3) that it is robust against random and
systematic noise. We further demonstrate the applicability of our algorithm to
two previously published clinical data-sets
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